docs: def ProtoFromTfRecordFiles(self,
                               files,
                               max_entries=10000,
                               features=None,
                               is_sequence=False,
                               iterator_options=None):
      """Creates a feature statistics proto from a set of TFRecord files.
  
      Args:
        files: A list of dicts describing files for each dataset for the proto.
          Each
            entry contains a 'path' field with the path to the TFRecord file on
              disk
            and a 'name' field to identify the dataset in the proto.
        max_entries: The maximum number of examples to load from each dataset
            in order to create the proto. Defaults to 10000.
        features: A list of strings that is a whitelist of feature names to create
            feature statistics for. If set to None then all features in the
              dataset
            are analyzed. Defaults to None.
        is_sequence: True if the input data from 'tables' are tf.SequenceExamples,
            False if tf.Examples. Defaults to false.
        iterator_options: Options to pass to the iterator that reads the examples.
            Defaults to None.
  
      Returns:
        The feature statistics proto for the provided files.
      """
      datasets = []
      for entry in files:
        entries, size = self._GetTfRecordEntries(entry['path'], max_entries,
                                                 is_sequence, iterator_options)
        datasets.append({'entries': entries, 'size': size, 'name': entry['name']})
      return self.GetDatasetsProto(datasets, features)